MEASURING THE EFFECTS OF A CYCLING PROGRAM ON QUALITY OF LIFE OF OLDER ADULTS LIVING IN LONG TERM CARE
Bibliographic record
Abstract
Abstract Cycling Without Age (CWA) is a program offered in long-term care (LTC) homes around the world that allows older adults who are unable to ride a bicycle the pleasure of a bike ride again. Two residents sit in the front bench seat of a trishaw, and a volunteer bike pilot pedals the bike. A variety of anecdotal benefits have been reported and no study has rigorously measured the effects of this program. The purpose of this research is to measure the effects of the CWA program on happiness and quality of life of LTC home residents, through observation of an existing program in a Canadian LTC home. A total of 24 residents were purposefully recruited in a biking group (n=23) who were biked twice a week for 12 weeks, and a strolls group (n=16) who went for outdoors walks or wheelchair rides for the same period of time. Data on pain, cognition, social engagement, and aggressive behaviour was harvested from the Resident-Assessment Instrument – Minimum Data Set (RAI-MDS). Happiness was measured pre and post all bike rides and strolls using a visual analogue scale, and the LTC QoL assessment was used to assess QOL. Findings show that biking group scored higher on happiness after bike rides compared to before, as well as compared to strolls. Bike group QOL scores are higher at the end of the 12 weeks than were strolls group. In summary, CWA shows potential to increase QOL and happiness of residents living in LTC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".